Application of single-cell sequencing to the research of tumor microenvironment

Smad4 Deficiency in S100A4+ Macrophages Enhances Colitis-associated Tumorigenesis by Promoting Macrophage Lipid Metabolism Augmented M2 Polarization

S100A4 is primarily expressed in intestinal macrophages, and promotes colonic inflammation and colitis-associated colon tumorigenesis. Smad4 is also expressed in the colon; however, it inhibits colitis-associated cancer (CAC) development. The specific role of Smad4 in S100A4+ cells in CAC remains unknown. In this study, an azoxymethane (AOM)/dextran sodium sulfate (DSS)-induced CAC model was established in mice with S100A4+ cell-specific Smad4 deletion (S100A4 Smad4-/-). Smad4 deficiency in S100A4+ cells exacerbated DSS-induced colitis and promoted colorectal tumorigenesis. In addition, S100A4+ cell-specific Smad4 ablation promoted the M2 polarization of macrophages in CAC. Mechanistically, Smad4 depletion in macrophages enhanced lipid metabolism by activating the FA binding protein 2 (Fabp2)/STAT6 pathway. Furthermore, Smad4 deficiency in macrophages promoted MC38 tumor growth in myeloid-specific Smad4 deficient (Lyz Smad4-/-) mice, whereas blocking Fabp2 expression reversed the tumor growth. Additionally, high Smad4 expression was associated with prolonged survival in patients with colorectal cancer. Thus, Smad4 in S100A4+ macrophages plays a tumor-inhibiting role in CAC development and supports its use as a prognostic marker in CRC patients.

Breast cancer genomic analyses reveal genes, mutations, and signaling networks

Breast cancer (BC) is the most commonly diagnosed cancer and the predominant cause of death in women. BC is a complex disorder, and the exploration of several types of BC omic data, highlighting genes, perturbations, signaling and cellular mechanisms, is needed. We collected mutational data from 9,555 BC samples using cBioPortal. We classified 1174 BC genes (mutated ≥ 40 samples) into five tiers (BCtier_I-V) and subjected them to pathway and protein‒protein network analyses using EnrichR and STRING 11, respectively. BCtier_I possesses 12 BC genes with mutational frequencies > 5%, with only 5 genes possessing > 10% frequencies, namely, PIK3CA (35.7%), TP53 (34.3%), GATA3 (11.5%), CDH1 (11.4%) and MUC16 (11%), and the next seven BC genes are KMT2C (8.8%), TTN (8%), MAP3K1 (8%), SYNE1 (7.2%), AHNAK2 (7%), USH2A (5.5%), and RYR2 (5.4%). Our pathway analyses revealed that the five top BC pathways were the PI3K-AKT, TP53, NOTCH, HIPPO, and RAS pathways. We found that BC panels share only seven genes. These findings show that BC arises from genetic disruptions evident in BC signaling and protein networks.

Immunofluorescence-Verified Sphingolipid Signatures Indicate Improved Prognosis in Liver Cancer Patients

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy, with its pathogenesis involving a complex interplay of molecular mechanisms, including cell cycle dysregulation, evasion of apoptosis, enhanced angiogenesis, and aberrant immune responses. Precision medicine approaches that target specific molecular subtypes through multi-omics integration hold promise for improving patient survival. Among the various molecular players, sphingolipids have emerged as pivotal regulators of tumor growth and apoptosis, positioning them as key targets in the search for novel anticancer therapies. Methods: To identify critical genes involved in sphingolipid metabolism (SM), we employed the AUCell algorithm and correlation analysis in conjunction with scRNA-seq data. A robust prognostic risk model was developed using Cox proportional hazards and Lasso regression, and its predictive performance was validated using an independent cohort from the International Cancer Genome Consortium (ICGC). The model's evaluation also incorporated analyses of the tumor microenvironment (TME), immunotherapy responses, mutational landscape, and pathway enrichment across different risk strata. Finally, we conducted multiplex immunofluorescence assays to investigate the functional role of ZC3HAV1 in HCC. Results: Our analysis yielded a 9-gene signature risk model with strong prognostic capabilities, effectively stratifying HCC patients into high- and low-risk groups, with significant differences in survival outcomes. Notably, the model revealed distinct variations in the immune microenvironment and responsiveness to immunotherapy between the risk groups. Further experimental validation identified ZC3HAV1 as a key gene, with multiplex immunofluorescence suggesting its involvement in promoting malignant progression in HCC through modulation of the epithelial-mesenchymal transition (EMT). Conclusion: This sphingolipid metabolism-based prognostic model is not only predictive of survival in HCC but also indicative of immunotherapy efficacy in certain patient subsets. Our findings underscore the crucial role of sphingolipid metabolism in shaping the immune microenvironment, offering new avenues for targeted therapeutic interventions.

Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges

Tumor heterogeneity refers to the diversity observed among tumor cells: both between different tumors (inter-tumor heterogeneity) and within a single tumor (intra-tumor heterogeneity). These cells can display distinct morphological and phenotypic characteristics, including variations in cellular morphology, metastatic potential and variability treatment responses among patients. Therefore, a comprehensive understanding of such heterogeneity is necessary for deciphering tumor-specific mechanisms that may be diagnostically and therapeutically valuable. Innovative and multidisciplinary approaches are needed to understand this complex feature. In this context, proteogenomics has been emerging as a significant resource for integrating omics fields such as genomics and proteomics. By combining data obtained from both Next-Generation Sequencing (NGS) technologies and mass spectrometry (MS) analyses, proteogenomics aims to provide a comprehensive view of tumor heterogeneity. This approach reveals molecular alterations and phenotypic features related to tumor subtypes, potentially identifying therapeutic biomarkers. Many achievements have been made; however, despite continuous advances in proteogenomics-based methodologies, several challenges remain: in particular the limitations in sensitivity and specificity and the lack of optimal study models. This review highlights the impact of proteogenomics on characterizing tumor phenotypes, focusing on the critical challenges and current limitations of its use in different clinical and preclinical models for tumor phenotypic characterization.

The shared role of neutrophils in ankylosing spondylitis and ulcerative colitis

This study aimed to analyze single-cell sequencing data to investigate immune cell interactions in ankylosing spondylitis (AS) and ulcerative colitis (UC). Vertebral bone marrow blood was collected from three AS patients for 10X single-cell sequencing. Analysis of single-cell data revealed distinct cell types in AS and UC patients. Cells significantly co-expressing immune cells (P < 0.05) were subjected to communication analysis. Overlapping genes of these co-expressing immune cells were subjected to GO and KEGG analyses. Key genes were identified using STRING and Cytoscape to assess their correlation with immune cell expression. The results showed the significance of neutrophils in both diseases (P < 0.01), with notable interactions identified through communication analysis. XBP1 emerged as a Hub gene for both diseases, with AUC values of 0.760 for AS and 0.933 for UC. Immunohistochemistry verified that the expression of XBP1 was significantly lower in the AS group and significantly greater in the UC group than in the control group (P < 0.01). This finding highlights the critical role of neutrophils in both AS and UC, suggesting the presence of shared immune response elements. The identification of XBP1 as a potential therapeutic target offers promising intervention avenues for both diseases.

Integrating single-cell transcriptomics and machine learning to predict breast cancer prognosis: A study based on natural killer cell-related genes

Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. This study aimed to establish a prognostic model using NK cell-related genes (NKRGs) by integrating single-cell transcriptomic data with machine learning. We identified 44 significantly expressed NKRGs involved in cytokine and T cell-related functions. Using 101 machine learning algorithms, the Lasso + RSF model showed the highest predictive accuracy with nine key NKRGs. We explored cell-to-cell communication using CellChat, assessed immune-related pathways and tumour microenvironment with gene set variation analysis and ssGSEA, and observed immune components by HE staining. Additionally, drug activity predictions identified potential therapies, and gene expression validation through immunohistochemistry and RNA-seq confirmed the clinical applicability of NKRGs. The nomogram showed high concordance between predicted and actual survival, linking higher tumour purity and risk scores to a reduced immune score. This NKRG-based model offers a novel approach for risk assessment and personalized treatment in BC, enhancing the potential of precision medicine.

Molecular Classifications in Gastric Cancer: A Call for Interdisciplinary Collaboration

Gastric cancer (GC) is a heterogeneous disease, often diagnosed at advanced stages, with a 5-year survival rate of approximately 20%. Despite notable technological advancements in cancer research over the past decades, their impact on GC management and outcomes has been limited. Numerous molecular alterations have been identified in GC, leading to various molecular classifications, such as those developed by The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG). Other authors have proposed alternative perspectives, including immune, proteomic, or epigenetic-based classifications. However, molecular stratification has not yet transitioned into clinical practice for GC, and little attention has been paid to alternative molecular classifications. In this review, we explore diverse molecular classifications in GC from a practical point of view, emphasizing their relationships with clinicopathological factors, prognosis, and therapeutic approaches. We have focused on classifications beyond those of TCGA and the ACRG, which have been less extensively reviewed previously. Additionally, we discuss the challenges that must be overcome to ensure their impact on patient treatment and prognosis. This review aims to serve as a practical framework to understand the molecular landscape of GC, facilitate the development of consensus molecular categories, and guide the design of innovative molecular studies in the field.

Natural Killer T Cell Diversity and Immunotherapy

Invariant natural killer T cells (iNKTs), a type of unconventional T cells, share features with NK cells and have an invariant T cell receptor (TCR), which recognizes lipid antigens loaded on CD1d molecules, a major histocompatibility complex class I (MHC-I)-like protein. This interaction produces the secretion of a wide array of cytokines by these cells, including interferon gamma (IFN-γ) and interleukin 4 (IL-4), allowing iNKTs to link innate with adaptive responses. Interestingly, molecules that bind CD1d have been identified that enable the modulation of these cells, highlighting their potential pro-inflammatory and immunosuppressive capacities, as required in different clinical settings. In this review, we summarize key features of iNKTs and current understandings of modulatory α-galactosylceramide (α-GalCer) variants, a model iNKT cell activator that can shift the outcome of adaptive immune responses. Furthermore, we discuss advances in the development of strategies that modulate these cells to target pathologies that are considerable healthcare burdens. Finally, we recapitulate findings supporting a role for iNKTs in infectious diseases and tumor immunotherapy.

Global research trends and prospects related to tumor microenvironment within Triple Negative Breast Cancer: a bibliometric analysis

Background and aims

The tumor microenvironment (TME) has pivotal parts within multiple tumor models of onset/progression, such as triple-negative breast cancer (TNBC). This bibliometric analysis was developed to explore trends and research niches revolving around TME in TNBC.

Methods

Web of Science Core Collection was queried for identifying studies linked with TME in TNBC, after which the VOSviewer, CiteSpace, and R software programs were used to conduct bibliometric analyses and to generate corresponding visualizations.

Results

In total, this study included 1,604 studies published from 2005-2023. The USA and China exhibited the highest numbers of citations, and the research institutions with the greatest output in this field included Harvard University, the University of Texas System, and Fudan University. Ying Wang from Sun Yat-Sen University was the most published and most cited author in this space. The highest number of articles were published in Cancer, while the greatest co-citation number was evident in Breast Cancer Research. Important keywords related to this research topic included metastasis, tumor-infiltrating lymphocytes, immunotherapy, chemotherapy, and nanoparticles. In particular, pembrolizumab, immunotherapy, nanoparticles, combination treatment, and biomarkers were topics of marked interest in recent reports.

Conclusion

The TME in TNBC is an area of rapidly growing and evolving research interest, with extensive global collaboration helping to drive this field forward. Antitumor therapies targeting the TME in TNBC patients represent an emerging topic of future research, providing opportunities for translational findings. The results of this analysis may provide additional guidance for work focused on the TME in TNBC.